Cosmological simulations may produce extremely large amount of data, such that its successful run depends on large storage capacity and huge I/O bandwidth, especially in the exascale computing scale. Effective error-bounded lossy compressors with both high compression ratios and low data distortion can significantly reduce the total data size while guaranteeing the data valid for post-analysis. In this poster, we propose a novel, efficient compression model for cosmological N-body simulation framework, by combining the advantages of both space-based compression and time-based compression. The evaluation with a well-known cosmological simulation code shows that our proposed solution can get much higher compression quality than other existing state-of-the-art compressors, with comparable compression/decompression rates.
S. Li et al., "Improving Error-Bounded Compression for Cosmological Simulation," Proceedings of the 30th ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (2018, Dallas, TX), Association for Computing Machinery (ACM), Nov 2018.
30th ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis, SC '18 (2018: Nov. 11-16, Dallas, TX)
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16 Nov 2018